IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v314y2022ics0306261922002884.html

Physics informed neural networks for control oriented thermal modeling of buildings

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Guo, Yanhua & Wang, Ningbo & Shao, Shuangquan & Huang, Congqi & Zhang, Zhentao & Li, Xiaoqiong & Wang, Youdong, 2024. "A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 204(C).
  2. Guo, Fangzhou & Li, Ao & Yue, Bao & Xiao, Ziwei & Xiao, Fu & Yan, Rui & Li, Anbang & Lv, Yan & Su, Bing, 2024. "Improving the out-of-sample generalization ability of data-driven chiller performance models using physics-guided neural network," Applied Energy, Elsevier, vol. 354(PA).
  3. Von Krannichfeldt, Leandro & Orehounig, Kristina & Fink, Olga, 2025. "Combining physics-based and data-driven modeling for building energy systems," Applied Energy, Elsevier, vol. 391(C).
  4. Chen, Chao & Zhang, Limao & Zhou, Cheng & Luo, Yongqiang, 2025. "Physics-informed explainable encoder-decoder deep learning for predictive estimation of building carbon emissions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
  5. Shi, Jihao & Li, Junjie & Zhang, Haoran & Yan, Jinyue, 2025. "Towards physics-guided graph neural network for hydrogen gas explosion simulation at urban scale," Applied Energy, Elsevier, vol. 401(PA).
  6. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
  7. Sha, Peng & Zhang, Yao & Wu, Xiao & Wang, Meihong & Shen, Jiong, 2025. "Modeling of propane thermal cracking process via physics-informed neural networks with process-consistent constraints," Energy, Elsevier, vol. 333(C).
  8. Chen, Dong & Chui, Chee-Kong & Lee, Poh Seng, 2025. "Adaptive physically consistent neural networks for data center thermal dynamics modeling," Applied Energy, Elsevier, vol. 377(PD).
  9. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
  10. Viorica Rozina Chifu & Tudor Cioara & Cristina Bianca Pop & Ionut Anghel & Andrei Pelle, 2024. "Physics-Informed Neural Networks for Heat Pump Load Prediction," Energies, MDPI, vol. 18(1), pages 1-20, December.
  11. Pandiyan, Surya Venkatesh & Gros, Sebastien & Rajasekharan, Jayaprakash, 2025. "Physics informed neural network based multi-zone electric water heater modeling for demand response," Applied Energy, Elsevier, vol. 380(C).
  12. Chen, Dongyu & Lin, Xiaojie & Qiao, Yiyuan, 2025. "Perspectives for artificial intelligence in sustainable energy systems," Energy, Elsevier, vol. 318(C).
  13. Taboga, Vincent & Gehring, Clement & Cam, Mathieu Le & Dagdougui, Hanane & Bacon, Pierre-Luc, 2024. "Neural differential equations for temperature control in buildings under demand response programs," Applied Energy, Elsevier, vol. 368(C).
  14. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
  15. Hu, Guoqing & You, Fengqi, 2024. "AI-enabled cyber-physical-biological systems for smart energy management and sustainable food production in a plant factory," Applied Energy, Elsevier, vol. 356(C).
  16. Li, Yanxin & He, Ning & He, Lile & Li, Ruoxia & Gao, Feng & Cheng, Fuan, 2025. "Self-triggered model predictive control for the thermal comfort and energy saving of office buildings," Energy, Elsevier, vol. 326(C).
  17. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).
  18. Saloux, Etienne & Candanedo, José A. & Vallianos, Charalampos & Morovat, Navid & Zhang, Kun, 2025. "From theory to practice: A critical review of model predictive control field implementations in the built environment," Applied Energy, Elsevier, vol. 393(C).
  19. Mukun Yuan & Jian Liu & Zheyuan Chen & Qingda Guo & Mingzhe Yuan & Jian Li & Guangping Yu, 2024. "Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE," Sustainability, MDPI, vol. 16(17), pages 1-27, August.
  20. Tang, Lingfeng & Xie, Haipeng & Wang, Yongguan & Xu, Zhanbo, 2025. "Deeply flexible commercial building HVAC system control: A physics-aware deep learning-embedded MPC approach," Applied Energy, Elsevier, vol. 388(C).
  21. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
  22. Zhang, Qingang & Zeng, Wei & Lin, Qinjie & Chng, Chin-Boon & Chui, Chee-Kong & Lee, Poh-Seng, 2023. "Deep reinforcement learning towards real-world dynamic thermal management of data centers," Applied Energy, Elsevier, vol. 333(C).
  23. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
  24. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2022. "Physically Consistent Neural Networks for building thermal modeling: Theory and analysis," Applied Energy, Elsevier, vol. 325(C).
  25. Rickard Brännvall & Jonas Gustafsson & Fredrik Sandin, 2023. "Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers," Energies, MDPI, vol. 16(5), pages 1-24, February.
  26. Xiao, Tianqi & You, Fengqi, 2024. "Physically consistent deep learning-based day-ahead energy dispatching and thermal comfort control for grid-interactive communities," Applied Energy, Elsevier, vol. 353(PB).
  27. Mu, Junjin & Yang, Chunjie & Yan, Feng & Wu, Yutong & Wang, Shaoqi & Zhao, Yuchen & Yan, Duojin, 2025. "Complex coupling representation in low-dimensional space for control-oriented energy-consuming industries modeling," Applied Energy, Elsevier, vol. 383(C).
  28. Li, Pengchao & Guo, Fang & Li, Yongfei & Yang, Xuejing & Yang, Xudong, 2025. "Physics-informed neural network for real-time thermal modeling of large-scale borehole thermal energy storage systems," Energy, Elsevier, vol. 315(C).
  29. Xinyue Xu & Julian Wang, 2025. "Comparative Analysis of Physics-Guided Bayesian Neural Networks for Uncertainty Quantification in Dynamic Systems," Forecasting, MDPI, vol. 7(1), pages 1-21, February.
  30. Murilo Eduardo Casteroba Bento, 2024. "Load Margin Assessment of Power Systems Using Physics-Informed Neural Network with Optimized Parameters," Energies, MDPI, vol. 17(7), pages 1-20, March.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.